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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
| Date | Stars |
|---|---|
| 2026-07-24 | 1818 |
| 2026-07-25 | 1818 |
| 2026-07-28 | 1818 |
| 2026-07-30 | 1818 |
| 2026-07-31 | 1818 |
| 2026-08-06 | 1818 |
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===
[](https://pypi.org/project/cogdl/)
[](https://app.travis-ci.com/THUDM/cogdl)
[](https://cogdl.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/cogdl)
[](https://coveralls.io/github/THUDM/cogdl?branch=master)
[](https://github.com/THUDM/cogdl/blob/master/LICENSE)
[](https://github.com/ambv/black)
**[Homepage](https://cogdl.ai)** | **[Paper](https://arxiv.org/abs/2103.00959)** | **[Documentation](https://cogdl.readthedocs.io)** | **[Discussion Forum](https://discuss.cogdl.ai)** | **[Dataset](./cogdl/datasets/README.md)** | **[中文](./README_CN.md)**
CogDL is a graph deep learning toolkit that allows researchers and developers to easily train and compare baseline or customized models for node classification, graph classification, and other important tasks in the graph domain.
We summarize the contributions of CogDL as follows:
- **Efficiency**: CogDL utilizes well-optimized operators to speed up training and save GPU memory of GNN models.
- **Ease of Use**: CogDL provides easy-to-use APIs for running experiments with the given models and datasets using hyper-parameter search.
- **Extensibility**: The design of CogDL makes it easy to apply GNN models to new scenarios based on our framework.
## ❗ News
- [The CogDL paper](https://arxiv.org/abs/2103.00959) was accepted by [WWW 2023](https://www2023.thewebconf.org/). Find us at WWW 2023! We also release the new **v0.6 release** which adds more examples of graph self-supervised learning, including [GraphMAE](https://github.com/THUDM/cogdl/tree/master/examples/graphmae), [GraphMAE2](https://github.com/THUDM/cogdl/tree/master/examples/graphmae2), and [BGRL](https://github.com/THUDM/cogdl/tree/master/examples/bgrl).
- A free GNN course provided by CogDL Team is present at [this link](https://cogdl.ai/gnn2022/). We also provide a [discussion forum](https://discuss.cogdl.ai) for Chinese users.
- The new **v0.5.3 release** supports mixed-precision training by setting \textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators.
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- The new **v0.5.2 release** adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.
- The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in [this link](./cogdl/datasets/rd2cd_data.py). 🎉
- The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 🎉
- The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
- The new **v0.4.0 release** refactors the data storage (from `Data` to `Graph`) and provides more fast operators to speed up GNN training. It also includes many self-suExcerpt of 13,072 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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